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Improving the sensitivity of quantum-enhanced interferometers through machine learning


   Cardiff School of Physics and Astronomy

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  Prof H Grote, Dr K Dooley  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

The UKRI CDT in Artificial Intelligence, Machine Learning and Advanced Computing (AIMLAC) aims at forming the next generation of AI innovators across a broad range of STEMM disciplines. The CDT provides advanced multi-disciplinary training in an inclusive, caring and open environment that nurture each individual student to achieve their full potential. Applications are encouraged from candidates from a diverse background that can positively contribute to the future of our society. 

Laser interferometers have long been used to probe fundamental physics phenomena, from disproving the existence of the theorized ether well over a hundred years ago to making the first direct detections of gravitational waves in recent years. Given the extraordinary measurement precision that can be achieved with the latest technological advances in laser interferometer design, they can now also be used to search for potential quantization of spacetime, ultra-high frequency gravitational waves and dark matter.

 A critical aspect of pushing the high-frequency sensitivity to new limits involves minimizing optical losses in the path of a squeezed light field injected at the output port of the interferometer. One such source of loss comes from imperfectly matched beam parameters. This project will require the student to use machine learning to train a set of a few dozen piezo-electric actuators to dynamically change the shape of a mirror. Other applications of machine learning can be explored, such as for adaptive control loops and complex noise analysis. The project will be at the cross-section of AI/ML and experiment.

Start date: 1st October 2022

The UKRI CDT in Artificial Intelligence, Machine Learning and Advanced Computing provides 4-year, fully funded PhD opportunities across broad research themes:

  • T1: data from large science facilities (particle physics, astronomy, cosmology)
  • T2: biological, health and clinical sciences (medical imaging, electronic health records, bioinformatics)
  • T3: novel mathematical, physical, and computer science approaches (data, hardware, software, algorithms)

 Its partner institutions are Swansea University (lead institution), Aberystwyth University, Bangor University, University of Bristol and Cardiff University.

Training in AI, high-performance computing (HPC) and high-performance data analytics (HPDA) plays an essential role, as does engagement with external partners, which include large international companies, locally based start-ups and SMEs, and government and Research Council partners. Training will be delivered via cohort activities across the partner institutions.

Positions are funded for 4 years, including 6-month placements with the external partners. The CDT will recruit 10 positions in 2022.

The partners include: We Predict, ATOS, DSTL, Mobileum, GCHQ, EDF, Amplyfi, DiRAC, Agxio, STFC, NVIDIA, Oracle, QinetiQ, Intel, IBM, Microsoft, Quantum Foundry, Dwr Cymru, TWI and many more.

More information, and a description of research projects, can be found at the UKRI CDT in Artificial Intelligence, Machine Learning & Advanced Computing website. http://cdt-aimlac.org/cdt-research.html

How to apply:

To apply, and for further details please visit the CDT website http://cdt-aimlac.org/cdt-apply.html and follow the instructions to apply online.

This includes an online application for this project at (with a start date of 1st October 2022): https://www.cardiff.ac.uk/study/postgraduate/research/programmes/programme/physics-and-astronomy)

Applicants should submit an application for postgraduate study via the Cardiff University webpages including:

• your academic CV

• a personal statement/covering letter

• two references, at least one of which should be academic

• Your degree certificates and transcripts to date.

In the "Research Proposal" section of your application, please specify the project title and supervisors of this project.

In the funding section, please select that you will not be self funding and write that the source of funding will be “AIMLAC CDT”

The deadline for applications for the UKRI CDT Scholarship in Artificial Intelligence, Machine Learning and Advanced Computing (AIMLAC) is 12th February 2022. However, AIMLAC will continue to accept applications until the positions are filled.

For general enquiries, please contact Rhian Melita Morris [Email Address Removed]

Eligibility:

The typical academic requirement is a minimum of a 2:1 physics and astronomy or a relevant discipline.

Applicants whose first language is not English are normally expected to meet the minimum University requirements (e.g. 6.5 IELTS) (https://www.cardiff.ac.uk/study/international/english-language-requirements)

Candidates should be interested in AI and big data challenges, and in (at least) one of the three research themes. You should have an aptitude and ability in computational thinking and methods (as evidenced by a degree in physics and astronomy, medical science, computer science, or mathematics, for instance) including the ability to write software (or willingness to learn it).

For more information on eligibility, please visit the UKRI CDT in Artificial Intelligence, Machine Learning & Advanced Computing website http://cdt-aimlac.org/


Funding Notes

The UK Research and Innovation (UKRI) fully-funded scholarships cover the full cost of 4 years tuition fees, a UKRI standard stipend of currently £15,921 per annum and additional funding for training, research and conference expenses. The scholarships are open to UK and international candidates.

References

"An experiment for observing quantum gravity phenomena using twin table-top 3D interferometers" Classical and Quantum Gravity, 38(8):085008 (2021); https://doi.org/10.1088/1361-6382/abe757
"Direct limits for scalar field dark matter from a gravitational-wave detector" https://arxiv.org/abs/2103.03783
"First Demonstration of 6 dB Quantum Noise Reduction in a Kilometer Scale Gravitational Wave Observatory" Physical Review Letters 126(4), 2021; https://doi.org/10.1103/PhysRevLett.126.041102

How good is research at Cardiff University in Physics?


Research output data provided by the Research Excellence Framework (REF)

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